transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
img = transform(Image.open(img))
image = img.unsqueeze(0)
#image = Variable(image).cuda()
image = Variable(image)
cnn = alexnet(embedding_dim=embedding_size)
rnn = RNN(embedding_dim=embedding_size,
hidden_dim=hidden_size,
vocab_size=vocab.index)
#cnn.cuda()
#rnn.cuda()
#cnn_file = str(train_time) + '_iter_' + str(epoch) + '_cnn.pkl'
#rnn_file = str(train_time) + '_iter_' + str(epoch) + '_rnn.pkl'
cnn_file = 'alex_iter_' + str(epoch) + '_cnn.pkl'
rnn_file = 'alex_iter_' + str(epoch) + '_rnn.pkl'
cnn.load_state_dict(
torch.load(os.path.join('train_file', cnn_file), map_location='cpu'))
rnn.load_state_dict(
torch.load(os.path.join('train_file', rnn_file), map_location='cpu'))
cnn_out = cnn(image)
word_id = rnn.search(cnn_out)
sentence = vocab.get_sentence(word_id)
print(sentence)
showimage = Image.open(args.img)
plt.imshow(np.asarray(showimage))
